Artificial intelligence in personalized medicine

Texte intégral


Artificial intelligence

in personalized medicine

Enrico Glaab



(1) Introduction: Artificial Intelligence vs. Classical Statistics

(2) AI success stories in recent years

(3) AI in personalized medicine:

- Biomarker discovery

- Drug discovery

- Digital health monitoring

(4) Common pitfalls and challenges


AI vs. Statistics

Artificial Intelligence

Machine Learning

Deep Learning


Enabling machines to think like humans

Training machines to learn a task

without explicit programming

ML using multi-layered

networks without manual


AI success stories: Image data (1)

• Image classification: unprecedented accuracies using deep learning

(e.g. “AlexNet” approach by Krizevsky et al., 2012)


AI success stories: Sequential data (2)

• Speech recognition: 30% less errors with new neural networks

(Huang et al., ACM Communications, 2012)

• Novel techniques: Recurrent Neural Networks (RNNs):


AI successes in personalized medicine - Biomarkers


Test approval

(FDA-cleared and/or LDT)




FDA-cleared, LDT

breast cancer



Van’t Veer et al.,

Nature, 2002

AlloMap Heart

FDA-cleared, LDT

identifying heart

transplant recipients

with risk of cellular


Yamani et al., J Heart

Lung Transplant, 2007

Prosigna Assay /


FDA-cleared, LDT

breast cancer risk of

distant recurrence


Nielsen et al., BMC

Cancer, 2014

Oncotype DX


breast cancer



Kelley et al., Cancer,




prostate cancer

metastatic risk


Marrone et al., PLoS

Curr., 2015


AI in biomedicine: Covid-19 hospital mortality prediction

• Covid-19: alterations in

common clinical blood

tests when diagnosed

• Apply ML à 90% accurate

predictions of mortality on

test set of 110 patients

(10 days in advance)


Deep learning for biomedical research: AlphaFold 2

• DeepMind’s AlphaFold 2 à major advance in protein structure prediction

• Scores > 90 in global distance test (GDT) in the CASP competition


AI in drug development


AI in drug development

• Question: Which drug-like compounds bind to a particular target protein?

• Example:

Active compounds

Inactive / low activity compounds



Application to SARS-CoV-2 compound screening

AI-based screening: Finding new inhibitors of viral proteins





AI for personalized health monitoring / digital biomarkers

Digital biomarkers:

- mobile phone gyrometer & accelerometer

- gait sensors (eGaIT system)


Recommendations from literature

Data pre-processing, filtering & normalization:




to check if pre-processing leads to information loss


compare or combine

multiple pre-processing approaches

Integration of prior knowledge & multi-omics analyses:


Assess cost/benefit

of multi-omics analyses using prior data, or conduct pilot analyses



existing software & frameworks

for integrative biological data analysis

Ensuring model interpretability & biological plausibility:


use dedicated methods to build

interpretable models

(e.g. rule learning methods)


Outlook: New AI strategies to address the challenges

Increase statistical power


AI-based integration of prior knowledge & multi-omics data


Algorithmic sample matching & selection, AI for batch adjustment

Increase model robustness


AI-based integration of models across human & animal model data,

different experimental platforms and cohorts (Transfer learning)

Increase model interpretability


Structured and graph-based machine learning



Biomedical Data Science Group

New members:


Thank you for your attention!



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